




"""simple docstring"""




import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


a_        =       logging.get_logger(__name__)

a_        =       {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a_        =       {
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
a_        =       {
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
a_        =       {
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

a_        =       {
    'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
a_        =       {
    'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
a_        =       {
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


a_        =       {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
a_        =       {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
a_        =       {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}

class   UpperCAmelCase_    (			snake_case		):
		UpperCamelCase							=VOCAB_FILES_NAMES
		UpperCamelCase							=CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase							=CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase							=CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class   UpperCAmelCase_    (			snake_case		):
		UpperCamelCase							=VOCAB_FILES_NAMES
		UpperCamelCase							=QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase							=QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase							=QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


a_        =       collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

a_        =       collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


a_        =       r'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '

@add_start_docstrings(snake_case		)
class   UpperCAmelCase_    :
		def __call__(						self				,						UpperCamelCase_				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						UpperCamelCase_ = False				,						UpperCamelCase_ = False				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						**UpperCamelCase_				,						)						->       BatchEncoding:
					if titles is None and texts is None:
								return super().__call__(
								    UpperCamelCase_				,						padding=UpperCamelCase_				,						truncation=UpperCamelCase_				,						max_length=UpperCamelCase_				,						return_tensors=UpperCamelCase_				,						return_attention_mask=UpperCamelCase_				,						**UpperCamelCase_				,						)
					elif titles is None or texts is None:
								__lowercase			:  int								=       titles if texts is None else texts
								return super().__call__(
								    UpperCamelCase_				,						UpperCamelCase_				,						padding=UpperCamelCase_				,						truncation=UpperCamelCase_				,						max_length=UpperCamelCase_				,						return_tensors=UpperCamelCase_				,						return_attention_mask=UpperCamelCase_				,						**UpperCamelCase_				,						)
					__lowercase			:  Optional[int]								=       titles if not isinstance(UpperCamelCase_				,						UpperCamelCase_       ) else [titles]
					__lowercase			:  Optional[int]								=       texts if not isinstance(UpperCamelCase_				,						UpperCamelCase_       ) else [texts]
					__lowercase			:  str								=       len(UpperCamelCase_       )
					__lowercase			:  List[Any]								=       questions if not isinstance(UpperCamelCase_				,						UpperCamelCase_       ) else [questions] * n_passages
					if len(UpperCamelCase_       ) != len(UpperCamelCase_       ):
								raise ValueError(
								    F"""There should be as many titles than texts but got {len(UpperCamelCase_       )} titles and {len(UpperCamelCase_       )} texts."""       )
					__lowercase			:  int								=       super().__call__(UpperCamelCase_				,						UpperCamelCase_				,						padding=UpperCamelCase_				,						truncation=UpperCamelCase_       )['''input_ids''']
					__lowercase			:  List[Any]								=       super().__call__(UpperCamelCase_				,						add_special_tokens=UpperCamelCase_				,						padding=UpperCamelCase_				,						truncation=UpperCamelCase_       )['''input_ids''']
					__lowercase			:  Optional[Any]								=       {
					    '''input_ids''': [
					        (encoded_question_and_title + encoded_text)[:max_length]
					        if max_length is not None and truncation
					        else encoded_question_and_title + encoded_text
					        for encoded_question_and_title, encoded_text in zip(UpperCamelCase_				,						UpperCamelCase_       )
					    ]
					}
					if return_attention_mask is not False:
								__lowercase			:  str								=       []
								for input_ids in encoded_inputs["input_ids"]:
											attention_mask.append([int(input_id != self.pad_token_id       ) for input_id in input_ids]       )
								__lowercase			:  List[str]								=       attention_mask
					return self.pad(UpperCamelCase_				,						padding=UpperCamelCase_				,						max_length=UpperCamelCase_				,						return_tensors=UpperCamelCase_       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_ = 16				,						UpperCamelCase_ = 64				,						UpperCamelCase_ = 4				,						)						->       List[DPRSpanPrediction]:
					__lowercase			:  List[Any]								=       reader_input['''input_ids''']
					__lowercase			,__lowercase			,__lowercase			:  List[str]								=       reader_output[:3]
					__lowercase			:  Optional[int]								=       len(UpperCamelCase_       )
					__lowercase			:  Any								=       sorted(range(UpperCamelCase_       )				,						reverse=UpperCamelCase_				,						key=relevance_logits.__getitem__       )
					__lowercase			:  List[DPRReaderOutput]								=       []
					for doc_id in sorted_docs:
								__lowercase			:  Any								=       list(input_ids[doc_id]       )
								# assuming question & title information is at the beginning of the sequence
								__lowercase			:  Tuple								=       sequence_ids.index(self.sep_token_id				,						2       ) + 1  # second sep id
								if sequence_ids[-1] == self.pad_token_id:
											__lowercase			:  Optional[Any]								=       sequence_ids.index(self.pad_token_id       )
								else:
											__lowercase			:  List[Any]								=       len(UpperCamelCase_       )

								__lowercase			:  List[str]								=       self._get_best_spans(
								    start_logits=start_logits[doc_id][passage_offset:sequence_len]				,						end_logits=end_logits[doc_id][passage_offset:sequence_len]				,						max_answer_length=UpperCamelCase_				,						top_spans=UpperCamelCase_				,						)
								for start_index, end_index in best_spans:
											start_index += passage_offset
											end_index += passage_offset
											nbest_spans_predictions.append(
											    DPRSpanPrediction(
											        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]				,						relevance_score=relevance_logits[doc_id]				,						doc_id=UpperCamelCase_				,						start_index=UpperCamelCase_				,						end_index=UpperCamelCase_				,						text=self.decode(sequence_ids[start_index : end_index + 1]       )				,						)       )
								if len(UpperCamelCase_       ) >= num_spans:
											break
					return nbest_spans_predictions[:num_spans]
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						)						->       List[DPRSpanPrediction]:
					__lowercase			:  Tuple								=       []
					for start_index, start_score in enumerate(UpperCamelCase_       ):
								for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]       ):
											scores.append(((start_index, start_index + answer_length), start_score + end_score)       )
					__lowercase			:  int								=       sorted(UpperCamelCase_				,						key=lambda UpperCamelCase_       : x[1]				,						reverse=UpperCamelCase_       )
					__lowercase			:  Optional[Any]								=       []
					for (start_index, end_index), score in scores:
								if start_index > end_index:
											raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""       )
								__lowercase			:  Any								=       end_index - start_index + 1
								if length > max_answer_length:
											raise ValueError(F"""Span is too long: {length} > {max_answer_length}"""       )
								if any(
								    start_index <= prev_start_index <= prev_end_index <= end_index
								    or prev_start_index <= start_index <= end_index <= prev_end_index
								    for (prev_start_index, prev_end_index) in chosen_span_intervals       ):
											continue
								chosen_span_intervals.append((start_index, end_index)       )

								if len(UpperCamelCase_       ) == top_spans:
											break
					return chosen_span_intervals


@add_end_docstrings(snake_case		)
class   UpperCAmelCase_    (			snake_case   ,					snake_case		):
		UpperCamelCase							=VOCAB_FILES_NAMES
		UpperCamelCase							=READER_PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase							=READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase							=READER_PRETRAINED_INIT_CONFIGURATION
		UpperCamelCase							=["input_ids", "attention_mask"]









'''simple docstring'''


import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}





class  __lowercase    (	__magic_name__			):
   _a    						=   """segformer"""







   def __init__(						self   ,				UpperCamelCase=3   ,				UpperCamelCase=4   ,				UpperCamelCase=[2, 2, 2, 2]   ,				UpperCamelCase=[8, 4, 2, 1]   ,				UpperCamelCase=[32, 64, 160, 256]   ,				UpperCamelCase=[7, 3, 3, 3]   ,				UpperCamelCase=[4, 2, 2, 2]   ,				UpperCamelCase=[1, 2, 5, 8]   ,				UpperCamelCase=[4, 4, 4, 4]   ,				UpperCamelCase="gelu"   ,				UpperCamelCase=0.0   ,				UpperCamelCase=0.0   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.02   ,				UpperCamelCase=0.1   ,				UpperCamelCase=1e-6   ,				UpperCamelCase=256   ,				UpperCamelCase=255   ,				**UpperCamelCase   ,				)					->     int:
     super().__init__(**UpperCamelCase   )

     if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
       warnings.warn(
           'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
           ' removed, as the behaviour will default to that of reshape_last_stage = True.'   ,				UpperCamelCase   ,				)

     __a								=       num_channels
     __a								=       num_encoder_blocks
     __a								=       depths
     __a								=       sr_ratios
     __a								=       hidden_sizes
     __a								=       patch_sizes
     __a								=       strides
     __a								=       mlp_ratios
     __a								=       num_attention_heads
     __a								=       hidden_act
     __a								=       hidden_dropout_prob
     __a								=       attention_probs_dropout_prob
     __a								=       classifier_dropout_prob
     __a								=       initializer_range
     __a								=       drop_path_rate
     __a								=       layer_norm_eps
     __a								=       decoder_hidden_size
     __a								=       kwargs.get('reshape_last_stage'   ,				UpperCamelCase   )
     __a								=       semantic_loss_ignore_index





class  __lowercase    (	__magic_name__			):
   _a    						=   version.parse("""1.11"""			)







   @property
   def 		UpperCamelCase__      (						self   )					->     Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ]   )







   @property
   def 		UpperCamelCase__      (						self   )					->     float:
     return 1e-4







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     return 12



